 |
| Artificial and robotic intelligence |
History of artificial intelligence
The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in processing and storage power.
Early AI research in the 1950s explored topics such as problem solving and symbolic methods. In the 1960s, the US Department of Defense became interested in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street map projects in the 1970s. And DARPA produced smart personal assistants in 2003, long before Siri, Alexa, or Cortana were household names.
This initial work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and intelligent search systems that can be designed to complement and augment human capabilities.
While Hollywood movies and sci-fi novels portray AI as human-like robots taking over the world, the current evolution of AI technologies is not that scary or that smart. Instead, AI has evolved to provide many specific benefits across industries.
Follow our blog for modern examples of AI in healthcare, retail, and more.
Why is artificial intelligence important?
AI automates repetitive learning and discovery through data. But AI is different from hardware-driven robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, and computerized tasks reliably and without fatigue. For this type of automation, human research is still essential to set up the system and ask the right questions.
AI adds intelligence to existing products. In most cases, the AI will not be sold as a standalone application. Rather, the products you already use will be enhanced with artificial intelligence capabilities, just as Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with massive amounts of data to improve many technologies in the home and workplace, from security intelligence to investment analysis AI adapts to through progressive learning algorithms to allow the data to do the programming.
The AI finds structure and regularities in the data so that the algorithm acquires a skill: the algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself to play chess, it can teach itself which product to recommend online next. And the models adapt when they are given new data. Backpropagation is a 1 ° technique that allows the model to fit, through training and aggregated data, when the first answer is not entirely correct.
AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computing power and big data. You need a lot of data to train deep learning models because they learn directly from the data. The more data you can feed into it, the more accurate it will be.
AI achieves incredible precision through deep neural networks, which was previously impossible. For example, their interactions with Alexa, Google Search, and Google Photos are based on deep learning, and they become more accurate the more we use them. In the medical field, the artificial intelligence techniques of deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same precision as highly trained radiologists.
AI makes the most of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. With the role of data now more important than ever, you can create a competitive advantage. If you have the best data in a competitive industry, even if everyone applies similar techniques, the best data will win.
Comments
Post a Comment